Trends and Outliers

Why Big Data Analytics Could Spell Big Energy Savings

Coming in July – the ban on incandescent light bulbs of yore. This law, which aims to kill off the light bulb made famous by Thomas Edison, is designed to increase the reliance on more energy-efficient incandescent bulbs and increase the use of compact fluorescent bulbs (CFL).

The CFL bulbs are projected to save about $600 million in energy costs per year (if every American replaces one incandescent with a CFL) and last up to seven years, according to the federal government’s ENERGY STAR program.

While the move to more energy-efficient lighting is a big cost cutter, it’s looking at the energy productivity of states, municipalities, homeowners and businesses that will really pay off, notes Tyler Hamilton (@Go2CleanBreak) at The Energy Collective.

The Alliance Commission on National Energy Efficiency Policy recommends “doubling energy productivity by 2020.” And the place to start is with big data analytics.

Hamilton says the first step toward increasing energy productivity is monitoring energy use, combined with “tighter building codes, stricter vehicle emission standards, serious attempts to recycle waste heat at industrial facilities and better tax breaks for companies that install more energy-efficient equipment.”

With data on usage, companies can save money and energy by retrofitting or optimizing the operation of their buildings. And data analytics can provide “information about how buildings function on a day-to-day, even minute-by-minute basis,” says Dan Seto, founder and president of CircuitMeter, a company that monitors and reports on energy usage.

In addition to monitoring for cost savings and energy productivity, companies are just beginning to break ground with big data on “predicting future generation” needs, notes Ben Holland (@BenInBoulder), program manager for the Rocky Mountain Institute’s Get Ready Project, an initiative aimed at integrating electric cars in cities across the US.

Holland predicts energy and big data will become synonymous, as we’re able to track “nearly everything that we do that uses energy.” He says, “We will see the data we’re capturing from electric utilities be used in a variety of ways including predicting future generation needs, balancing renewable energy loads or suggesting measures to consumers for reducing energy use.”

One area of promise in this laundry list of energy-saving uses for big data is monitoring “plug loads,” Michael Bendewald (@MikeBendewald) a consultant with RMI, tells Holland. He suggests measuring plug loads – what tenants and occupants plug into the wall – could give us guidelines for reducing energy use.

But Holland points out that this must be handled with care – “people still want hot showers and cold beer.” However, “entrepreneurs and startups can harness the power of big data analytics to provide those services in a cleaner, more efficient manner,” he adds.

Next Steps:

• Get answers to questions about energy consumption like: “How has world energy consumption grown and changed over the last 45 years? or “What are projections for world energy markets to 2030?” in our World Energy Survey Analysis demo.
• Subscribe to our blog to stay up to date on big data analytics and energy productivity.

Amanda Brandon
Spotfire Blogging Team

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